Originally posted 2/1/2011
recently argued on the importance of keeping population health outcomes and determinants separate, since the former represents today’s health and the latter tomorrows’ health. This week and next, I’ll be digging a bit deeper to look first at outcomes and then later at determinants.
Health outcomes have been defined as “all the possible results that may stem from exposure to a causal factor from preventive or therapeutic interventions.” As Gib Parrish noted in his 2010 award-winning article, there are many different ways to measure health outcomes. These include:
- Life expectancy from birth
- Age-adjusted or age –specific mortality rates
- Condition-specific changes in life expectancy and mortality rates
- Self-reports such as general level of health
Parrish notes that outcome metrics should present both the overall level of health of a population and the distribution of health among different geographic, economic, and demographic groups in the population.
The MATCH population health model underpinning this blog reflects much of this perspective. The 2X2 outcome diagram below includes both mortality and non-mortality (i.e., health related quality of life) components, as well as both population mean and population disparity metrics. This makes sense conceptually, but poses issues and choices in practice.
Of course, it is possible to separately track outcome measures in these four (or more) spaces. This is what many health planning exercises do, such as the recently released Healthy People 2020 (HP2020). In this important national process, a large number of individual measures of general health status as well as health related quality of life and health disparities will be monitored and reported on periodically.
One HP2020 indicator is “Healthy Life Expectancy,” which will be assessed through three distinct measures:
- expected years of life in good or better health
- expected years of life free of limitation of activity
- expected years of life free of selected chronic diseases
These three metrics are “summary” health outcome measures, combining mortality and health related quality of life together. In a similar approach, our County Health Rankings’ summary outcome measure assigns 50% to years of potential life lost before age 75 and 50% to 4 non-mortality measures. These weights reflect our interpretation and judgement of the relative importance of the components, but we recognize that not everyone would agree with our choices. There is no empiric “right” answer to the question “Which Outcomes Should We Improve?”. This is a question of values; decisions weighting length of life versus quality of life should be made by individuals and communities.
Why does this matter? Different outcome choices require different patterns of investment in the determinants of such health outcomes. I’d like to see a web-based tool that allows communities to explore a broad set of outcomes and that leads them through a process of selecting outcome priorities that have local meaning and relevance. Having a clear sense of place-based outcome goals should help establish policy priorities and guide resource allocation.
As readers of this blog know, we don’t yet precisely know which programs and policies are most cost effective for overall population heath improvement – let alone those that reduce disparities (stay tuned for more soon on this topic). But we are hopelessly lost if we aren’t clear on where we are going, and what our targets are. Clear and transparent choices about outcomes have tremendous promise, serving as a sort of population health compass to guide us step by step toward a healthier tomorrow.